plot.boot                package:boot                R Documentation

_P_l_o_t_s _o_f _t_h_e _O_u_t_p_u_t _o_f _a _B_o_o_t_s_t_r_a_p _S_i_m_u_l_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     This takes a bootstrap object and produces plots for the bootstrap
     replicates of the variable of interest.

_U_s_a_g_e:

     ## S3 method for class 'boot':
     plot(x, index=1, t0=NULL, t=NULL, jack=FALSE, qdist="norm", 
          nclass=NULL, df, ...)

_A_r_g_u_m_e_n_t_s:

       x: An object of class '"boot"' returned from one of the
          bootstrap generation functions. 

   index: The index of the variable of interest within the output of
          'boot.out'.  This is ignored if 't' and 't0' are supplied. 

      t0: The original value of the statistic.  This defaults to
          'boot.out$t0[index]' unless 't' is supplied when it defaults
          to 'NULL'. In that case no vertical  line is drawn on the
          histogram. 

       t: The bootstrap replicates of the statistic.  Usually this will
          take on its default value of 'boot.out$t[,index]', however it
          may be useful sometimes  to supply a different set of values
          which are a function of 'boot.out$t'. 

    jack: A logical value indicating whether a
          jackknife-after-bootstrap plot is  required.  The default is
          not to produce such a plot. 

   qdist: The distribution against which the Q-Q plot should be drawn. 
          At present  '"norm"' (normal distribution - the default) and
          '"chisq"' (chi-squared  distribution) are the only possible
          values. 

  nclass: An integer giving the number of classes to be used in the
          bootstrap histogram. The default is the integer between 10
          and 100 closest to 'ceiling(length(t)/25)'.  

      df: If 'qdist' is '"chisq"' then this is the degrees of freedom
          for the chi-squared distribution to be used.  It is a
          required argument in that case. 

     ...: When 'jack' is 'TRUE' additional parameters to
          'jack.after.boot' can be  supplied.  See the help file for
          'jack.after.boot' for details of the possible parameters. 

_D_e_t_a_i_l_s:

     This function will generally produce two side-by-side plots.  The
     left plot will be a histogram of the bootstrap replicates. 
     Usually the breaks of the histogram will be chosen so that 't0' is
     at a breakpoint and all intervals are of equal length.  A vertical
     dotted line indicates the position of 't0'. This cannot be done if
     't' is supplied but 't0' is not and so, in that case,  the
     breakpoints are computed by 'hist' using the 'nclass' argument and
     no vertical line is drawn.

     The second plot is a Q-Q plot of the bootstrap replicates.  The
     order  statistics of the replicates can be plotted against normal
     or chi-squared quantiles.  In either case the expected line is
     also plotted.  For the normal, this will have intercept 'mean(t)'
     and slope 'sqrt(var(t))' while for the chi-squared it has
     intercept 0 and slope 1.  

     If 'jack' is 'TRUE' a third plot is produced beneath these two. 
     That plot is the jackknife-after-bootstrap plot.  This plot may
     only be requested when nonparametric simulation has been used. 
     See 'jack.after.boot' for further details of this plot.

_V_a_l_u_e:

     'boot.out' is returned invisibly.

_S_i_d_e _E_f_f_e_c_t_s:

     All screens are closed and cleared and a number of plots are
     produced on the  current graphics device.  Screens are closed but
     not cleared at termination of this function.

_S_e_e _A_l_s_o:

     'boot', 'jack.after.boot', 'print.boot'

_E_x_a_m_p_l_e_s:

     # We fit an exponential model to the air-conditioning data and use
     # that for a parametric bootstrap.  Then we look at plots of the
     # resampled means.
     air.rg <- function(data, mle)
          rexp(length(data), 1/mle)

     data(aircondit)
     air.boot <- boot(aircondit$hours, mean, R=999, sim="parametric",
                      ran.gen=air.rg, mle=mean(aircondit$hours))
     plot(air.boot)

     # In the difference of means example for the last two series of the 
     # gravity data
     data(gravity)
     grav1 <- gravity[as.numeric(gravity[,2])>=7,]
     grav.fun <- function(dat, w)
     {    strata <- tapply(dat[, 2], as.numeric(dat[, 2]))
          d <- dat[, 1]
          ns <- tabulate(strata)
          w <- w/tapply(w, strata, sum)[strata]
          mns <- tapply(d * w, strata, sum)
          mn2 <- tapply(d * d * w, strata, sum)
          s2hat <- sum((mn2 - mns^2)/ns)
          c(mns[2]-mns[1],s2hat)
     }

     grav.boot <- boot(grav1, grav.fun, R=499, stype="w", strata=grav1[,2])
     plot(grav.boot)
     # now suppose we want to look at the studentized differences.
     grav.z <- (grav.boot$t[,1]-grav.boot$t0[1])/sqrt(grav.boot$t[,2])
     plot(grav.boot,t=grav.z,t0=0)

     # In this example we look at the one of the partial correlations for the
     # head dimensions in the dataset frets.
     pcorr <- function( x )
     { 
     #  Function to find the correlations and partial correlations between
     #  the four measurements.
          v <- cor(x);
          v.d <- diag(var(x));
          iv <- solve(v);
          iv.d <- sqrt(diag(iv));
          iv <- - diag(1/iv.d) %*% iv %*% diag(1/iv.d);
          q <- NULL; 
          n <- nrow(v);
          for (i in 1:(n-1)) 
               q <- rbind( q, c(v[i,1:i],iv[i,(i+1):n]) );
          q <- rbind( q, v[n,] );
          diag(q) <- round(diag(q));
          q
     }

     frets.fun <- function( data, i )
     {    d <- data[i,];
          v <- pcorr( d );
          c(v[1,],v[2,],v[3,],v[4,])
     }
     data(frets)
     frets.boot <- boot(log(as.matrix(frets)), frets.fun, R=999)
     plot(frets.boot, index=7, jack=TRUE, stinf=FALSE, useJ=FALSE)

